Parsing is All You Need for Accurate Gait Recognition in the Wild

TL;DR

This paper introduces Gait Parsing Sequence (GPS), a high-entropy gait representation derived from human parsing, and a novel recognition framework, ParsingGait, which significantly improves gait recognition accuracy in real-world scenarios.

Contribution

The paper proposes GPS as a new gait representation and ParsingGait as a novel recognition framework, along with the first parsing-based gait dataset for wild environments.

Findings

GPS significantly improves gait recognition accuracy.

ParsingGait outperforms existing methods on Gait3D-Parsing.

The new dataset enables better evaluation of gait recognition in the wild.

Abstract

Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in gait recognition for in-the-lab environments, they usually fail in real-world scenarios due to their low information entropy for gait representations. To achieve accurate gait recognition in the wild, this paper presents a novel gait representation, named Gait Parsing Sequence (GPS). GPSs are sequences of fine-grained human segmentation, i.e., human parsing, extracted from video frames, so they have much higher information entropy to encode the shapes and dynamics of fine-grained human parts during walking. Moreover, to effectively explore the capability of the GPS representation, we propose a novel human parsing-based gait recognition framework, named ParsingGait. ParsingGait contains a Convolutional Neural Network (CNN)-based backbone and two light-weighted heads. The first head extracts global semantic features from GPSs, while the other one learns mutual information of part-level features through Graph Convolutional Networks to model the detailed dynamics of human walking. Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively evaluate our method and existing gait recognition methods. The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait. The code and dataset are available at https://gait3d.github.io/gait3d-parsing-hp .